Examining the influencing factors of CO2 emissions at city level via panel quantile regression: evidence from 102 Chinese cities

2019 ◽  
Vol 51 (35) ◽  
pp. 3906-3919 ◽  
Author(s):  
Haitao Zheng ◽  
Jie Hu ◽  
Shanshan Wang ◽  
Huiwen Wang
Keyword(s):  
Quantile Regression ◽  
Co2 Emissions ◽  
Influencing Factors ◽  
Chinese Cities ◽  
Panel Quantile Regression

scholarly journals International Tourism, Digital Infrastructure, and CO2 Emissions: Fresh Evidence From Panel Quantile Regression Approach

2021 ◽  
Author(s):  
Liu Wei ◽  
Sana ullah
Keyword(s):  
Quantile Regression ◽  
Regression Model ◽  
Co2 Emissions ◽  
Environmental Quality ◽  
Quantile Regression Model ◽  
Digital Infrastructure ◽  
Robust Model ◽  
Tourism Sector ◽  
Panel Quantile Regression ◽  
Regression Techniques

Abstract The main motivation behind this study is the importance of the tourism sector and digitalization in the economic development of a country and their potential effects on the country's environmental quality. For empirical analysis, the study applies FMOLS, DOLS, and quantile regression techniques for Asian economies. The findings of the study confirmed that tourism and digitalization improve environmental quality in FMOLS and DOLS models. In the basic quantile regression model, the estimates attached to tourism arrival are positive 5th quantile to 40th quantile and then turn negative from 60th quantile and onwards. Likewise, the estimates attached to tourism receipts in the robust quantile regression model are positive from quantile 5th to quantile 20 and negative and increasing from quantile 30 and onwards. Conversely, the estimates of digital infrastructure are insignificant in the basic quantile model at all quantiles except 95th. However, the estimated coefficients of digital infrastructure in the robust model are negative and rising from 40th quantile to 70th quantile and negative and declining from 80th quantile to 95th quantile. In general, we can say that as the tourism and digital sectors grow, the CO2 emissions decline.

The Asymmetric Effects of Regional House Prices in the UK: New Evidence from Panel Quantile Regression Framework

2021 ◽  
pp. 232102222098054
Author(s):  
Panayiotis Tzeremes
Keyword(s):  
Private Sector ◽  
Quantile Regression ◽  
House Prices ◽  
Ripple Effect ◽  
The United Kingdom ◽  
Housing Starts ◽  
Panel Quantile Regression ◽  
Linear Behaviour ◽  
Significant Performance ◽  
The Uk

This study unfurls the non-linear behaviour of regional house prices in the United Kingdom by employing quarterly observations spanning the period 1992Q1–2017Q4. Our enquiry aims at examining UK house prices within a multivariable framework and, more specifically, by employing panel quantile regression with fixed effect. In brief, the empirical findings obtained from these methodologies indicate that the UK house prices are influenced at lower and upper quantiles, and that precisely they are influenced by variables such as income, private sector housing starts and employment. We highly support that there is a strong heterogeneity among UK regions and that asymmetry may be one of the keys of the ripple effect. Particularly, the income shows a positively significant performance at lower and higher regional house prices. Moreover, the variables private sector housing starts and employment rate are statistically significant for house prices. Leveraging for first-time panel quantile regression for the case of regional house prices in the UK, policymakers will have a profound understanding of regional house prices. JEL Classifications: C22, R21, R31

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